Overview

Dataset statistics

Number of variables27
Number of observations13576
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.0 MiB
Average record size in memory1.4 KiB

Variable types

Numeric11
Text3
DateTime6
Categorical7

Alerts

journey_start_country has constant value ""Constant
journey_end_country has constant value ""Constant
passenger_seats has constant value ""Constant
journey_start_lon is highly overall correlated with journey_start_towngroup and 2 other fieldsHigh correlation
journey_start_lat is highly overall correlated with journey_start_department and 3 other fieldsHigh correlation
journey_start_insee is highly overall correlated with journey_start_department and 3 other fieldsHigh correlation
journey_start_department is highly overall correlated with journey_start_lat and 4 other fieldsHigh correlation
journey_end_lon is highly overall correlated with journey_end_towngroupHigh correlation
journey_end_lat is highly overall correlated with journey_end_department and 1 other fieldsHigh correlation
journey_end_insee is highly overall correlated with journey_end_department and 1 other fieldsHigh correlation
journey_end_department is highly overall correlated with journey_end_lat and 2 other fieldsHigh correlation
journey_distance is highly overall correlated with journey_duration and 4 other fieldsHigh correlation
journey_duration is highly overall correlated with journey_distance and 4 other fieldsHigh correlation
journey_start_towngroup is highly overall correlated with journey_start_lon and 7 other fieldsHigh correlation
journey_end_towngroup is highly overall correlated with journey_end_lon and 5 other fieldsHigh correlation
operator_class is highly overall correlated with journey_start_lon and 7 other fieldsHigh correlation
has_incentive is highly overall correlated with journey_start_lon and 7 other fieldsHigh correlation
journey_start_towngroup is highly imbalanced (67.4%)Imbalance
journey_end_towngroup is highly imbalanced (68.6%)Imbalance
operator_class is highly imbalanced (86.7%)Imbalance
has_incentive is highly imbalanced (97.6%)Imbalance
journey_start_lat is highly skewed (γ1 = -20.23214872)Skewed
journey_id has unique valuesUnique

Reproduction

Analysis started2023-10-14 11:17:08.195513
Analysis finished2023-10-14 11:17:55.158328
Duration46.96 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

journey_id
Real number (ℝ)

UNIQUE 

Distinct13576
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17538178
Minimum17277395
Maximum17830294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size212.1 KiB
2023-10-14T12:17:55.330008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum17277395
5-th percentile17303732
Q117406263
median17537688
Q317669394
95-th percentile17780700
Maximum17830294
Range552899
Interquartile range (IQR)263131.25

Descriptive statistics

Standard deviation152150.21
Coefficient of variation (CV)0.0086753713
Kurtosis-1.1740166
Mean17538178
Median Absolute Deviation (MAD)131657
Skewness0.03825169
Sum2.3809831 × 1011
Variance2.3149686 × 1010
MonotonicityNot monotonic
2023-10-14T12:17:55.667020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17277395 1
 
< 0.1%
17623454 1
 
< 0.1%
17623500 1
 
< 0.1%
17623499 1
 
< 0.1%
17623572 1
 
< 0.1%
17623623 1
 
< 0.1%
17623811 1
 
< 0.1%
17623881 1
 
< 0.1%
17624816 1
 
< 0.1%
17625120 1
 
< 0.1%
Other values (13566) 13566
99.9%
ValueCountFrequency (%)
17277395 1
< 0.1%
17277418 1
< 0.1%
17277506 1
< 0.1%
17277507 1
< 0.1%
17277546 1
< 0.1%
17277875 1
< 0.1%
17278265 1
< 0.1%
17278370 1
< 0.1%
17278565 1
< 0.1%
17278573 1
< 0.1%
ValueCountFrequency (%)
17830294 1
< 0.1%
17824962 1
< 0.1%
17823785 1
< 0.1%
17822269 1
< 0.1%
17819012 1
< 0.1%
17815420 1
< 0.1%
17811281 1
< 0.1%
17811058 1
< 0.1%
17810638 1
< 0.1%
17810374 1
< 0.1%
Distinct11893
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2023-10-14T12:17:56.176641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters488736
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10459 ?
Unique (%)77.0%

Sample

1st rowd2a0a6fe-b8fa-4a1b-aae6-a21ca99c10d3
2nd row8ed2d495-7515-4546-8cb0-19a464122142
3rd rowd2ddd2f4-bdd5-4e81-94bc-a2974a4f5dcb
4th rowd2ddd2f4-bdd5-4e81-94bc-a2974a4f5dcb
5th row74fe92cd-c8c8-4e75-89ab-89408f3e2ffd
ValueCountFrequency (%)
0f9e7c9a-d1e5-477d-b213-05146fd1f746 16
 
0.1%
505046e1-2bbd-42c9-99c7-59f18bea8e8e 4
 
< 0.1%
41c95bfe-b06c-424f-ad1f-67317997225d 3
 
< 0.1%
728c57ba-5db5-4818-b398-0b30f28cf447 3
 
< 0.1%
36ae534a-7cca-4b7e-9b7e-d53fc8d2b6df 3
 
< 0.1%
c0b94672-a5b5-42eb-a3b7-3d729e7c5479 3
 
< 0.1%
1ade0d6a-4237-454d-8414-253a6f8a608c 3
 
< 0.1%
ba1f2ff7-9388-4788-9787-f136f3af7991 3
 
< 0.1%
ceed2e70-976d-4ba2-8791-f091339fa014 3
 
< 0.1%
02536c9c-fa9b-45d5-b372-f0259f647975 3
 
< 0.1%
Other values (11883) 13532
99.7%
2023-10-14T12:17:56.976152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 54304
 
11.1%
4 39031
 
8.0%
8 28889
 
5.9%
b 28801
 
5.9%
a 28678
 
5.9%
9 28653
 
5.9%
2 25778
 
5.3%
f 25702
 
5.3%
e 25661
 
5.3%
7 25654
 
5.2%
Other values (7) 177585
36.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 274987
56.3%
Lowercase Letter 159445
32.6%
Dash Punctuation 54304
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 39031
14.2%
8 28889
10.5%
9 28653
10.4%
2 25778
9.4%
7 25654
9.3%
1 25583
9.3%
0 25523
9.3%
5 25407
9.2%
6 25251
9.2%
3 25218
9.2%
Lowercase Letter
ValueCountFrequency (%)
b 28801
18.1%
a 28678
18.0%
f 25702
16.1%
e 25661
16.1%
d 25526
16.0%
c 25077
15.7%
Dash Punctuation
ValueCountFrequency (%)
- 54304
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 329291
67.4%
Latin 159445
32.6%

Most frequent character per script

Common
ValueCountFrequency (%)
- 54304
16.5%
4 39031
11.9%
8 28889
8.8%
9 28653
8.7%
2 25778
7.8%
7 25654
7.8%
1 25583
7.8%
0 25523
7.8%
5 25407
7.7%
6 25251
7.7%
Latin
ValueCountFrequency (%)
b 28801
18.1%
a 28678
18.0%
f 25702
16.1%
e 25661
16.1%
d 25526
16.0%
c 25077
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 488736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 54304
 
11.1%
4 39031
 
8.0%
8 28889
 
5.9%
b 28801
 
5.9%
a 28678
 
5.9%
9 28653
 
5.9%
2 25778
 
5.3%
f 25702
 
5.3%
e 25661
 
5.3%
7 25654
 
5.2%
Other values (7) 177585
36.3%
Distinct3044
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Memory size212.1 KiB
Minimum2023-08-01 00:40:00+02:00
Maximum2023-08-31 23:50:00+02:00
2023-10-14T12:17:57.316381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:57.671046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size212.1 KiB
Minimum2023-08-01 00:00:00
Maximum2023-08-31 00:00:00
2023-10-14T12:17:57.974354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:58.303008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
Distinct144
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size212.1 KiB
Minimum2023-10-14 00:00:00
Maximum2023-10-14 23:50:00
2023-10-14T12:17:58.626590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:58.967701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

journey_start_lon
Real number (ℝ)

HIGH CORRELATION 

Distinct609
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0768307
Minimum-0.358
Maximum3.267
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)< 0.1%
Memory size212.1 KiB
2023-10-14T12:17:59.256649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.358
5-th percentile0.866
Q11.063
median1.087
Q31.113
95-th percentile1.228
Maximum3.267
Range3.625
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.1563683
Coefficient of variation (CV)0.1452116
Kurtosis56.481075
Mean1.0768307
Median Absolute Deviation (MAD)0.025
Skewness1.5101926
Sum14619.053
Variance0.024451046
MonotonicityNot monotonic
2023-10-14T12:17:59.430989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.103 256
 
1.9%
1.078 256
 
1.9%
1.08 223
 
1.6%
1.087 218
 
1.6%
1.084 194
 
1.4%
1.095 193
 
1.4%
1.077 184
 
1.4%
1.092 182
 
1.3%
1.079 181
 
1.3%
1.094 180
 
1.3%
Other values (599) 11509
84.8%
ValueCountFrequency (%)
-0.358 1
 
< 0.1%
-0.351 2
 
< 0.1%
-0.349 1
 
< 0.1%
-0.342 1
 
< 0.1%
0.093 1
 
< 0.1%
0.114 3
< 0.1%
0.115 2
 
< 0.1%
0.141 1
 
< 0.1%
0.142 1
 
< 0.1%
0.181 5
< 0.1%
ValueCountFrequency (%)
3.267 16
0.1%
2.477 1
 
< 0.1%
2.384 1
 
< 0.1%
2.187 1
 
< 0.1%
2.081 1
 
< 0.1%
2.072 1
 
< 0.1%
2.068 1
 
< 0.1%
2.01 1
 
< 0.1%
1.713 5
 
< 0.1%
1.568 3
 
< 0.1%

journey_start_lat
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct515
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.41922
Minimum43.919
Maximum49.974
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size212.1 KiB
2023-10-14T12:17:59.619799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum43.919
5-th percentile49.249
Q149.417
median49.438
Q349.449
95-th percentile49.563
Maximum49.974
Range6.055
Interquartile range (IQR)0.032

Descriptive statistics

Standard deviation0.21334615
Coefficient of variation (CV)0.0043170684
Kurtosis518.29593
Mean49.41922
Median Absolute Deviation (MAD)0.015
Skewness-20.232149
Sum670915.33
Variance0.045516581
MonotonicityNot monotonic
2023-10-14T12:17:59.985129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.441 383
 
2.8%
49.447 380
 
2.8%
49.446 374
 
2.8%
49.437 355
 
2.6%
49.436 351
 
2.6%
49.438 343
 
2.5%
49.443 319
 
2.3%
49.445 302
 
2.2%
49.439 299
 
2.2%
49.434 291
 
2.1%
Other values (505) 10179
75.0%
ValueCountFrequency (%)
43.919 16
0.1%
48.803 1
 
< 0.1%
48.873 1
 
< 0.1%
48.885 1
 
< 0.1%
48.901 1
 
< 0.1%
48.976 5
 
< 0.1%
49.001 7
0.1%
49.005 12
0.1%
49.006 1
 
< 0.1%
49.007 1
 
< 0.1%
ValueCountFrequency (%)
49.974 4
 
< 0.1%
49.93 1
 
< 0.1%
49.927 1
 
< 0.1%
49.923 1
 
< 0.1%
49.922 1
 
< 0.1%
49.921 14
0.1%
49.92 7
0.1%
49.919 17
0.1%
49.918 2
 
< 0.1%
49.863 1
 
< 0.1%

journey_start_insee
Real number (ℝ)

HIGH CORRELATION 

Distinct259
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70420.649
Minimum12082
Maximum93010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size212.1 KiB
2023-10-14T12:18:00.317800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12082
5-th percentile27375
Q176319
median76540
Q376540
95-th percentile76640
Maximum93010
Range80928
Interquartile range (IQR)221

Descriptive statistics

Standard deviation16186.226
Coefficient of variation (CV)0.22985057
Kurtosis3.3274503
Mean70420.649
Median Absolute Deviation (MAD)0
Skewness-2.3026253
Sum9.5603073 × 108
Variance2.6199392 × 108
MonotonicityNot monotonic
2023-10-14T12:18:00.655705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76540 6844
50.4%
27701 442
 
3.3%
76057 290
 
2.1%
76451 219
 
1.6%
76319 218
 
1.6%
76709 208
 
1.5%
76575 200
 
1.5%
76322 181
 
1.3%
76377 139
 
1.0%
76705 134
 
1.0%
Other values (249) 4701
34.6%
ValueCountFrequency (%)
12082 16
0.1%
14118 3
 
< 0.1%
14333 1
 
< 0.1%
14341 2
 
< 0.1%
14366 1
 
< 0.1%
27003 28
0.2%
27008 6
 
< 0.1%
27016 2
 
< 0.1%
27056 1
 
< 0.1%
27062 12
0.1%
ValueCountFrequency (%)
93010 1
 
< 0.1%
78362 5
 
< 0.1%
78073 1
 
< 0.1%
76759 1
 
< 0.1%
76758 19
0.1%
76756 11
0.1%
76753 16
0.1%
76750 2
 
< 0.1%
76743 15
0.1%
76740 4
 
< 0.1%

journey_start_department
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.949028
Minimum12
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size212.1 KiB
2023-10-14T12:18:00.964829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile27
Q176
median76
Q376
95-th percentile76
Maximum93
Range81
Interquartile range (IQR)0

Descriptive statistics

Standard deviation16.168434
Coefficient of variation (CV)0.23114594
Kurtosis3.325264
Mean69.949028
Median Absolute Deviation (MAD)0
Skewness-2.3025595
Sum949628
Variance261.41825
MonotonicityNot monotonic
2023-10-14T12:18:01.234823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
76 11895
87.6%
27 1646
 
12.1%
12 16
 
0.1%
14 7
 
0.1%
78 6
 
< 0.1%
60 4
 
< 0.1%
75 1
 
< 0.1%
93 1
 
< 0.1%
ValueCountFrequency (%)
12 16
 
0.1%
14 7
 
0.1%
27 1646
 
12.1%
60 4
 
< 0.1%
75 1
 
< 0.1%
76 11895
87.6%
78 6
 
< 0.1%
93 1
 
< 0.1%
ValueCountFrequency (%)
93 1
 
< 0.1%
78 6
 
< 0.1%
76 11895
87.6%
75 1
 
< 0.1%
60 4
 
< 0.1%
27 1646
 
12.1%
14 7
 
0.1%
12 16
 
0.1%
Distinct259
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2023-10-14T12:18:01.600168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length5
Mean length9.4516058
Min length2

Characters and Unicode

Total characters128315
Distinct characters57
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)0.3%

Sample

1st rowRouen
2nd rowRouen
3rd rowVarneville-Bretteville
4th rowVarneville-Bretteville
5th rowLe Mesnil-Esnard
ValueCountFrequency (%)
rouen 6844
46.4%
le 681
 
4.6%
val-de-reuil 442
 
3.0%
barentin 290
 
2.0%
mont-saint-aignan 219
 
1.5%
grand-couronne 218
 
1.5%
trait 208
 
1.4%
saint-ã‰tienne-du-rouvray 200
 
1.4%
la 184
 
1.2%
grand-quevilly 181
 
1.2%
Other values (261) 5268
35.8%
2023-10-14T12:18:02.346596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 17036
13.3%
n 13564
10.6%
u 12383
 
9.7%
o 11193
 
8.7%
R 8004
 
6.2%
i 7308
 
5.7%
l 7256
 
5.7%
- 7012
 
5.5%
a 6521
 
5.1%
r 5422
 
4.2%
Other values (47) 32616
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 97913
76.3%
Uppercase Letter 20599
 
16.1%
Dash Punctuation 7012
 
5.5%
Space Separator 1159
 
0.9%
Modifier Symbol 527
 
0.4%
Other Symbol 493
 
0.4%
Control 452
 
0.4%
Other Punctuation 132
 
0.1%
Initial Punctuation 19
 
< 0.1%
Currency Symbol 9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17036
17.4%
n 13564
13.9%
u 12383
12.6%
o 11193
11.4%
i 7308
7.5%
l 7256
7.4%
a 6521
 
6.7%
r 5422
 
5.5%
t 4005
 
4.1%
s 2595
 
2.7%
Other values (13) 10630
10.9%
Uppercase Letter
ValueCountFrequency (%)
R 8004
38.9%
S 1509
 
7.3%
à 1498
 
7.3%
B 1396
 
6.8%
L 1162
 
5.6%
M 886
 
4.3%
C 850
 
4.1%
V 733
 
3.6%
G 694
 
3.4%
P 690
 
3.3%
Other values (13) 3177
 
15.4%
Modifier Symbol
ValueCountFrequency (%)
¨ 488
92.6%
´ 39
 
7.4%
Other Symbol
ValueCountFrequency (%)
© 487
98.8%
® 6
 
1.2%
Control
ValueCountFrequency (%)
‰ 450
99.6%
“ 2
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 7012
100.0%
Space Separator
ValueCountFrequency (%)
1159
100.0%
Other Punctuation
ValueCountFrequency (%)
' 132
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 19
100.0%
Currency Symbol
ValueCountFrequency (%)
¢ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 118512
92.4%
Common 9803
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17036
14.4%
n 13564
11.4%
u 12383
10.4%
o 11193
9.4%
R 8004
 
6.8%
i 7308
 
6.2%
l 7256
 
6.1%
a 6521
 
5.5%
r 5422
 
4.6%
t 4005
 
3.4%
Other values (36) 25820
21.8%
Common
ValueCountFrequency (%)
- 7012
71.5%
1159
 
11.8%
¨ 488
 
5.0%
© 487
 
5.0%
‰ 450
 
4.6%
' 132
 
1.3%
´ 39
 
0.4%
« 19
 
0.2%
¢ 9
 
0.1%
® 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125315
97.7%
None 3000
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 17036
13.6%
n 13564
10.8%
u 12383
9.9%
o 11193
 
8.9%
R 8004
 
6.4%
i 7308
 
5.8%
l 7256
 
5.8%
- 7012
 
5.6%
a 6521
 
5.2%
r 5422
 
4.3%
Other values (37) 29616
23.6%
None
ValueCountFrequency (%)
à 1498
49.9%
¨ 488
 
16.3%
© 487
 
16.2%
‰ 450
 
15.0%
´ 39
 
1.3%
« 19
 
0.6%
¢ 9
 
0.3%
® 6
 
0.2%
Ã… 2
 
0.1%
“ 2
 
0.1%

journey_start_towngroup
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct32
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Métropole Rouen Normandie
10193 
CA Seine-Eure
 
875
CC Inter-Caux-Vexin
 
727
CC Roumois Seine
 
438
CC Caux - Austreberthe
 
419
Other values (27)
 
924

Length

Max length45
Median length26
Mean length24.201385
Min length13

Characters and Unicode

Total characters328558
Distinct characters56
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowMétropole Rouen Normandie
2nd rowMétropole Rouen Normandie
3rd rowCC Terroir de Caux
4th rowCC Terroir de Caux
5th rowMétropole Rouen Normandie

Common Values

ValueCountFrequency (%)
Métropole Rouen Normandie 10193
75.1%
CA Seine-Eure 875
 
6.4%
CC Inter-Caux-Vexin 727
 
5.4%
CC Roumois Seine 438
 
3.2%
CC Caux - Austreberthe 419
 
3.1%
CA Evreux Portes de Normandie 139
 
1.0%
CC Terroir de Caux 108
 
0.8%
CC Yvetot Normandie 103
 
0.8%
CC Lyons Andelle 98
 
0.7%
CU Le Havre Seine Métropole 97
 
0.7%
Other values (22) 379
 
2.8%

Length

2023-10-14T12:18:02.707060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
normandie 10495
25.8%
mã©tropole 10292
25.3%
rouen 10193
25.1%
cc 2069
 
5.1%
ca 1205
 
3.0%
seine-eure 875
 
2.2%
inter-caux-vexin 727
 
1.8%
seine 653
 
1.6%
caux 615
 
1.5%
440
 
1.1%
Other values (59) 3038
 
7.5%

Most occurring characters

ValueCountFrequency (%)
o 43065
13.1%
e 39214
 
11.9%
27026
 
8.2%
r 24254
 
7.4%
n 24114
 
7.3%
u 13699
 
4.2%
i 13656
 
4.2%
a 12505
 
3.8%
t 12473
 
3.8%
m 11210
 
3.4%
Other values (46) 107342
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 232011
70.6%
Uppercase Letter 56035
 
17.1%
Space Separator 27026
 
8.2%
Other Symbol 10535
 
3.2%
Dash Punctuation 2865
 
0.9%
Modifier Symbol 29
 
< 0.1%
Decimal Number 26
 
< 0.1%
Other Punctuation 26
 
< 0.1%
Currency Symbol 3
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 43065
18.6%
e 39214
16.9%
r 24254
10.5%
n 24114
10.4%
u 13699
 
5.9%
i 13656
 
5.9%
a 12505
 
5.4%
t 12473
 
5.4%
m 11210
 
4.8%
d 11052
 
4.8%
Other values (13) 26769
11.5%
Uppercase Letter
ValueCountFrequency (%)
R 10719
19.1%
à 10567
18.9%
N 10514
18.8%
M 10297
18.4%
C 6881
12.3%
A 1888
 
3.4%
S 1529
 
2.7%
E 1086
 
1.9%
V 763
 
1.4%
I 736
 
1.3%
Other values (11) 1055
 
1.9%
Other Punctuation
ValueCountFrequency (%)
/ 18
69.2%
. 5
 
19.2%
' 3
 
11.5%
Modifier Symbol
ValueCountFrequency (%)
¨ 26
89.7%
´ 3
 
10.3%
Space Separator
ValueCountFrequency (%)
27026
100.0%
Other Symbol
ValueCountFrequency (%)
© 10535
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2865
100.0%
Decimal Number
ValueCountFrequency (%)
4 26
100.0%
Currency Symbol
ValueCountFrequency (%)
¢ 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 288046
87.7%
Common 40512
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 43065
15.0%
e 39214
13.6%
r 24254
 
8.4%
n 24114
 
8.4%
u 13699
 
4.8%
i 13656
 
4.7%
a 12505
 
4.3%
t 12473
 
4.3%
m 11210
 
3.9%
d 11052
 
3.8%
Other values (34) 82804
28.7%
Common
ValueCountFrequency (%)
27026
66.7%
© 10535
 
26.0%
- 2865
 
7.1%
4 26
 
0.1%
¨ 26
 
0.1%
/ 18
 
< 0.1%
. 5
 
< 0.1%
´ 3
 
< 0.1%
' 3
 
< 0.1%
¢ 3
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 307424
93.6%
None 21134
 
6.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 43065
14.0%
e 39214
12.8%
27026
 
8.8%
r 24254
 
7.9%
n 24114
 
7.8%
u 13699
 
4.5%
i 13656
 
4.4%
a 12505
 
4.1%
t 12473
 
4.1%
m 11210
 
3.6%
Other values (41) 86208
28.0%
None
ValueCountFrequency (%)
à 10567
50.0%
© 10535
49.8%
¨ 26
 
0.1%
´ 3
 
< 0.1%
¢ 3
 
< 0.1%

journey_start_country
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size941.3 KiB
France
13576 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters81456
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowFrance
3rd rowFrance
4th rowFrance
5th rowFrance

Common Values

ValueCountFrequency (%)
France 13576
100.0%

Length

2023-10-14T12:18:03.029233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T12:18:03.337137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
france 13576
100.0%

Most occurring characters

ValueCountFrequency (%)
F 13576
16.7%
r 13576
16.7%
a 13576
16.7%
n 13576
16.7%
c 13576
16.7%
e 13576
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67880
83.3%
Uppercase Letter 13576
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 13576
20.0%
a 13576
20.0%
n 13576
20.0%
c 13576
20.0%
e 13576
20.0%
Uppercase Letter
ValueCountFrequency (%)
F 13576
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81456
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 13576
16.7%
r 13576
16.7%
a 13576
16.7%
n 13576
16.7%
c 13576
16.7%
e 13576
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 13576
16.7%
r 13576
16.7%
a 13576
16.7%
n 13576
16.7%
c 13576
16.7%
e 13576
16.7%
Distinct3056
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size212.1 KiB
Minimum2023-08-01 00:50:00+02:00
Maximum2023-09-01 00:00:00+02:00
2023-10-14T12:18:03.584946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:18:03.939245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct32
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size212.1 KiB
Minimum2023-08-01 00:00:00
Maximum2023-09-01 00:00:00
2023-10-14T12:18:04.241339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:18:04.570313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
Distinct144
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size212.1 KiB
Minimum2023-10-14 00:00:00
Maximum2023-10-14 23:50:00
2023-10-14T12:18:04.889828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:18:05.222486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

journey_end_lon
Real number (ℝ)

HIGH CORRELATION 

Distinct594
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0760085
Minimum-0.351
Maximum3.085
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size212.1 KiB
2023-10-14T12:18:05.543465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.351
5-th percentile0.877
Q11.065
median1.09
Q31.112
95-th percentile1.223
Maximum3.085
Range3.436
Interquartile range (IQR)0.047

Descriptive statistics

Standard deviation0.14175201
Coefficient of variation (CV)0.13173874
Kurtosis27.330992
Mean1.0760085
Median Absolute Deviation (MAD)0.023
Skewness-1.9216702
Sum14607.892
Variance0.020093631
MonotonicityNot monotonic
2023-10-14T12:18:05.876250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.093 336
 
2.5%
1.103 279
 
2.1%
1.107 241
 
1.8%
1.078 238
 
1.8%
1.092 227
 
1.7%
1.087 224
 
1.6%
1.084 208
 
1.5%
1.08 207
 
1.5%
1.095 199
 
1.5%
1.101 188
 
1.4%
Other values (584) 11229
82.7%
ValueCountFrequency (%)
-0.351 1
 
< 0.1%
-0.35 1
 
< 0.1%
0.08 1
 
< 0.1%
0.097 1
 
< 0.1%
0.112 1
 
< 0.1%
0.143 2
 
< 0.1%
0.18 1
 
< 0.1%
0.181 19
 
0.1%
0.182 2
 
< 0.1%
0.183 71
0.5%
ValueCountFrequency (%)
3.085 1
< 0.1%
3.079 2
< 0.1%
2.177 2
< 0.1%
2.106 1
< 0.1%
2.095 1
< 0.1%
2.081 2
< 0.1%
2.006 2
< 0.1%
2.004 1
< 0.1%
1.996 1
< 0.1%
1.779 1
< 0.1%

journey_end_lat
Real number (ℝ)

HIGH CORRELATION 

Distinct514
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.424
Minimum44.115
Maximum50.353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size212.1 KiB
2023-10-14T12:18:06.216759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum44.115
5-th percentile49.245
Q149.419
median49.438
Q349.448
95-th percentile49.563
Maximum50.353
Range6.238
Interquartile range (IQR)0.029

Descriptive statistics

Standard deviation0.11834133
Coefficient of variation (CV)0.0023944102
Kurtosis598.57647
Mean49.424
Median Absolute Deviation (MAD)0.013
Skewness-13.539209
Sum670980.22
Variance0.01400467
MonotonicityNot monotonic
2023-10-14T12:18:06.547813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.44 456
 
3.4%
49.437 422
 
3.1%
49.441 413
 
3.0%
49.446 369
 
2.7%
49.443 356
 
2.6%
49.438 351
 
2.6%
49.447 345
 
2.5%
49.436 309
 
2.3%
49.434 305
 
2.2%
49.439 302
 
2.2%
Other values (504) 9948
73.3%
ValueCountFrequency (%)
44.115 2
 
< 0.1%
48.891 2
 
< 0.1%
48.976 4
< 0.1%
49 1
 
< 0.1%
49.001 2
 
< 0.1%
49.002 3
 
< 0.1%
49.005 8
0.1%
49.006 2
 
< 0.1%
49.011 6
< 0.1%
49.012 1
 
< 0.1%
ValueCountFrequency (%)
50.353 1
 
< 0.1%
49.974 4
 
< 0.1%
49.927 1
 
< 0.1%
49.926 1
 
< 0.1%
49.921 22
0.2%
49.92 6
 
< 0.1%
49.919 11
0.1%
49.918 1
 
< 0.1%
49.917 1
 
< 0.1%
49.915 6
 
< 0.1%

journey_end_insee
Real number (ℝ)

HIGH CORRELATION 

Distinct254
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70834.284
Minimum12145
Maximum92050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size212.1 KiB
2023-10-14T12:18:06.812897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12145
5-th percentile27375
Q176322
median76540
Q376540
95-th percentile76681
Maximum92050
Range79905
Interquartile range (IQR)218

Descriptive statistics

Standard deviation15663.277
Coefficient of variation (CV)0.22112565
Kurtosis3.835483
Mean70834.284
Median Absolute Deviation (MAD)0
Skewness-2.4131868
Sum9.6164624 × 108
Variance2.4533826 × 108
MonotonicityNot monotonic
2023-10-14T12:18:07.013913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76540 7050
51.9%
27701 452
 
3.3%
76057 277
 
2.0%
76451 248
 
1.8%
76575 213
 
1.6%
76709 195
 
1.4%
76705 191
 
1.4%
76319 180
 
1.3%
76322 174
 
1.3%
27375 142
 
1.0%
Other values (244) 4454
32.8%
ValueCountFrequency (%)
12145 2
 
< 0.1%
14341 2
 
< 0.1%
14715 1
 
< 0.1%
27003 33
0.2%
27008 5
 
< 0.1%
27011 2
 
< 0.1%
27016 1
 
< 0.1%
27056 1
 
< 0.1%
27058 2
 
< 0.1%
27062 8
 
0.1%
ValueCountFrequency (%)
92050 2
 
< 0.1%
78362 4
 
< 0.1%
76759 2
 
< 0.1%
76758 18
0.1%
76756 14
0.1%
76753 19
0.1%
76752 1
 
< 0.1%
76750 20
0.1%
76743 26
0.2%
76740 8
 
0.1%

journey_end_department
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.356659
Minimum12
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size212.1 KiB
2023-10-14T12:18:07.316383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile27
Q176
median76
Q376
95-th percentile76
Maximum92
Range80
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15.647557
Coefficient of variation (CV)0.22240336
Kurtosis3.835616
Mean70.356659
Median Absolute Deviation (MAD)0
Skewness-2.4134174
Sum955162
Variance244.84604
MonotonicityNot monotonic
2023-10-14T12:18:07.591329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
76 12001
88.4%
27 1555
 
11.5%
60 8
 
0.1%
78 4
 
< 0.1%
14 3
 
< 0.1%
12 2
 
< 0.1%
92 2
 
< 0.1%
59 1
 
< 0.1%
ValueCountFrequency (%)
12 2
 
< 0.1%
14 3
 
< 0.1%
27 1555
 
11.5%
59 1
 
< 0.1%
60 8
 
0.1%
76 12001
88.4%
78 4
 
< 0.1%
92 2
 
< 0.1%
ValueCountFrequency (%)
92 2
 
< 0.1%
78 4
 
< 0.1%
76 12001
88.4%
60 8
 
0.1%
59 1
 
< 0.1%
27 1555
 
11.5%
14 3
 
< 0.1%
12 2
 
< 0.1%
Distinct254
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2023-10-14T12:18:07.955292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length5
Mean length9.2795374
Min length2

Characters and Unicode

Total characters125979
Distinct characters58
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.2%

Sample

1st rowLe Petit-Quevilly
2nd rowFranqueville-Saint-Pierre
3rd rowRouen
4th rowRouen
5th rowRouen
ValueCountFrequency (%)
rouen 7050
48.1%
le 690
 
4.7%
val-de-reuil 452
 
3.1%
barentin 277
 
1.9%
mont-saint-aignan 248
 
1.7%
saint-ã‰tienne-du-rouvray 213
 
1.5%
trait 195
 
1.3%
tourville-la-riviã¨re 191
 
1.3%
grand-couronne 180
 
1.2%
grand-quevilly 174
 
1.2%
Other values (256) 4982
34.0%
2023-10-14T12:18:08.706260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 16756
13.3%
n 13543
10.8%
u 12392
 
9.8%
o 11169
 
8.9%
R 8247
 
6.5%
i 7382
 
5.9%
l 7161
 
5.7%
- 6850
 
5.4%
a 6109
 
4.8%
r 5079
 
4.0%
Other values (48) 31291
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 96062
76.3%
Uppercase Letter 20376
 
16.2%
Dash Punctuation 6850
 
5.4%
Space Separator 1076
 
0.9%
Modifier Symbol 561
 
0.4%
Control 460
 
0.4%
Other Symbol 452
 
0.4%
Other Punctuation 103
 
0.1%
Currency Symbol 18
 
< 0.1%
Initial Punctuation 17
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16756
17.4%
n 13543
14.1%
u 12392
12.9%
o 11169
11.6%
i 7382
7.7%
l 7161
7.5%
a 6109
 
6.4%
r 5079
 
5.3%
t 3947
 
4.1%
v 2583
 
2.7%
Other values (13) 9941
10.3%
Uppercase Letter
ValueCountFrequency (%)
R 8247
40.5%
à 1511
 
7.4%
S 1488
 
7.3%
B 1289
 
6.3%
L 1124
 
5.5%
M 904
 
4.4%
C 771
 
3.8%
V 761
 
3.7%
A 635
 
3.1%
G 633
 
3.1%
Other values (13) 3013
 
14.8%
Modifier Symbol
ValueCountFrequency (%)
¨ 516
92.0%
´ 45
 
8.0%
Control
ValueCountFrequency (%)
‰ 459
99.8%
“ 1
 
0.2%
Other Symbol
ValueCountFrequency (%)
© 437
96.7%
® 15
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 6850
100.0%
Space Separator
ValueCountFrequency (%)
1076
100.0%
Other Punctuation
ValueCountFrequency (%)
' 103
100.0%
Currency Symbol
ValueCountFrequency (%)
¢ 18
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 17
100.0%
Other Letter
ValueCountFrequency (%)
ª 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 116442
92.4%
Common 9537
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16756
14.4%
n 13543
11.6%
u 12392
10.6%
o 11169
9.6%
R 8247
 
7.1%
i 7382
 
6.3%
l 7161
 
6.1%
a 6109
 
5.2%
r 5079
 
4.4%
t 3947
 
3.4%
Other values (37) 24657
21.2%
Common
ValueCountFrequency (%)
- 6850
71.8%
1076
 
11.3%
¨ 516
 
5.4%
‰ 459
 
4.8%
© 437
 
4.6%
' 103
 
1.1%
´ 45
 
0.5%
¢ 18
 
0.2%
« 17
 
0.2%
® 15
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122955
97.6%
None 3024
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 16756
13.6%
n 13543
11.0%
u 12392
10.1%
o 11169
 
9.1%
R 8247
 
6.7%
i 7382
 
6.0%
l 7161
 
5.8%
- 6850
 
5.6%
a 6109
 
5.0%
r 5079
 
4.1%
Other values (37) 28267
23.0%
None
ValueCountFrequency (%)
à 1511
50.0%
¨ 516
 
17.1%
‰ 459
 
15.2%
© 437
 
14.5%
´ 45
 
1.5%
¢ 18
 
0.6%
« 17
 
0.6%
® 15
 
0.5%
ª 4
 
0.1%
Ã… 1
 
< 0.1%

journey_end_towngroup
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Métropole Rouen Normandie
10387 
CA Seine-Eure
 
903
CC Inter-Caux-Vexin
 
638
CC Caux - Austreberthe
 
419
CC Roumois Seine
 
338
Other values (26)
 
891

Length

Max length45
Median length26
Mean length24.332278
Min length13

Characters and Unicode

Total characters330335
Distinct characters53
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowMétropole Rouen Normandie
2nd rowMétropole Rouen Normandie
3rd rowMétropole Rouen Normandie
4th rowMétropole Rouen Normandie
5th rowMétropole Rouen Normandie

Common Values

ValueCountFrequency (%)
Métropole Rouen Normandie 10387
76.5%
CA Seine-Eure 903
 
6.7%
CC Inter-Caux-Vexin 638
 
4.7%
CC Caux - Austreberthe 419
 
3.1%
CC Roumois Seine 338
 
2.5%
CA Evreux Portes de Normandie 137
 
1.0%
CU Le Havre Seine Métropole 124
 
0.9%
CC Terroir de Caux 96
 
0.7%
CC Lyons Andelle 80
 
0.6%
CC Communauté Bray-Eawy 79
 
0.6%
Other values (21) 375
 
2.8%

Length

2023-10-14T12:18:09.263596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
normandie 10666
26.2%
mã©tropole 10513
25.8%
rouen 10387
25.5%
cc 1814
 
4.5%
ca 1243
 
3.1%
seine-eure 903
 
2.2%
inter-caux-vexin 638
 
1.6%
caux 600
 
1.5%
seine 579
 
1.4%
432
 
1.1%
Other values (57) 2895
 
7.1%

Most occurring characters

ValueCountFrequency (%)
o 43642
13.2%
e 39460
 
11.9%
27094
 
8.2%
r 24566
 
7.4%
n 24235
 
7.3%
u 13724
 
4.2%
i 13620
 
4.1%
a 12584
 
3.8%
t 12544
 
3.8%
m 11305
 
3.4%
Other values (43) 107561
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 233672
70.7%
Uppercase Letter 56016
 
17.0%
Space Separator 27094
 
8.2%
Other Symbol 10765
 
3.3%
Dash Punctuation 2708
 
0.8%
Modifier Symbol 34
 
< 0.1%
Decimal Number 32
 
< 0.1%
Other Punctuation 12
 
< 0.1%
Currency Symbol 1
 
< 0.1%
Control 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 10818
19.3%
à 10800
19.3%
N 10687
19.1%
M 10517
18.8%
C 6332
11.3%
A 1901
 
3.4%
S 1482
 
2.6%
E 1119
 
2.0%
V 652
 
1.2%
I 646
 
1.2%
Other values (12) 1062
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
o 43642
18.7%
e 39460
16.9%
r 24566
10.5%
n 24235
10.4%
u 13724
 
5.9%
i 13620
 
5.8%
a 12584
 
5.4%
t 12544
 
5.4%
m 11305
 
4.8%
d 11182
 
4.8%
Other values (11) 26810
11.5%
Modifier Symbol
ValueCountFrequency (%)
¨ 32
94.1%
´ 2
 
5.9%
Other Punctuation
ValueCountFrequency (%)
/ 11
91.7%
' 1
 
8.3%
Space Separator
ValueCountFrequency (%)
27094
100.0%
Other Symbol
ValueCountFrequency (%)
© 10765
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2708
100.0%
Decimal Number
ValueCountFrequency (%)
4 32
100.0%
Currency Symbol
ValueCountFrequency (%)
¢ 1
100.0%
Control
ValueCountFrequency (%)
“ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 289688
87.7%
Common 40647
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 43642
15.1%
e 39460
13.6%
r 24566
 
8.5%
n 24235
 
8.4%
u 13724
 
4.7%
i 13620
 
4.7%
a 12584
 
4.3%
t 12544
 
4.3%
m 11305
 
3.9%
d 11182
 
3.9%
Other values (33) 82826
28.6%
Common
ValueCountFrequency (%)
27094
66.7%
© 10765
 
26.5%
- 2708
 
6.7%
4 32
 
0.1%
¨ 32
 
0.1%
/ 11
 
< 0.1%
´ 2
 
< 0.1%
' 1
 
< 0.1%
¢ 1
 
< 0.1%
“ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308733
93.5%
None 21602
 
6.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 43642
14.1%
e 39460
12.8%
27094
 
8.8%
r 24566
 
8.0%
n 24235
 
7.8%
u 13724
 
4.4%
i 13620
 
4.4%
a 12584
 
4.1%
t 12544
 
4.1%
m 11305
 
3.7%
Other values (36) 85959
27.8%
None
ValueCountFrequency (%)
à 10800
50.0%
© 10765
49.8%
¨ 32
 
0.1%
´ 2
 
< 0.1%
¢ 1
 
< 0.1%
Ã… 1
 
< 0.1%
“ 1
 
< 0.1%

journey_end_country
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size941.3 KiB
France
13576 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters81456
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowFrance
3rd rowFrance
4th rowFrance
5th rowFrance

Common Values

ValueCountFrequency (%)
France 13576
100.0%

Length

2023-10-14T12:18:09.582946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T12:18:09.882303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
france 13576
100.0%

Most occurring characters

ValueCountFrequency (%)
F 13576
16.7%
r 13576
16.7%
a 13576
16.7%
n 13576
16.7%
c 13576
16.7%
e 13576
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67880
83.3%
Uppercase Letter 13576
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 13576
20.0%
a 13576
20.0%
n 13576
20.0%
c 13576
20.0%
e 13576
20.0%
Uppercase Letter
ValueCountFrequency (%)
F 13576
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81456
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 13576
16.7%
r 13576
16.7%
a 13576
16.7%
n 13576
16.7%
c 13576
16.7%
e 13576
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 13576
16.7%
r 13576
16.7%
a 13576
16.7%
n 13576
16.7%
c 13576
16.7%
e 13576
16.7%

passenger_seats
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size875.0 KiB
1
13576 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13576
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 13576
100.0%

Length

2023-10-14T12:18:10.131576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T12:18:10.434444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 13576
100.0%

Most occurring characters

ValueCountFrequency (%)
1 13576
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13576
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13576
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13576
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13576
100.0%

operator_class
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size875.0 KiB
C
13147 
B
 
411
A
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters13576
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 13147
96.8%
B 411
 
3.0%
A 18
 
0.1%

Length

2023-10-14T12:18:10.693924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T12:18:10.987141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
c 13147
96.8%
b 411
 
3.0%
a 18
 
0.1%

Most occurring characters

ValueCountFrequency (%)
C 13147
96.8%
B 411
 
3.0%
A 18
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13576
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 13147
96.8%
B 411
 
3.0%
A 18
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 13576
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 13147
96.8%
B 411
 
3.0%
A 18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 13147
96.8%
B 411
 
3.0%
A 18
 
0.1%

journey_distance
Real number (ℝ)

HIGH CORRELATION 

Distinct6537
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23725.623
Minimum1838
Maximum781647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size212.1 KiB
2023-10-14T12:18:11.217318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1838
5-th percentile4173.5
Q110920.25
median20414
Q329825
95-th percentile60322
Maximum781647
Range779809
Interquartile range (IQR)18904.75

Descriptive statistics

Standard deviation32052.276
Coefficient of variation (CV)1.3509561
Kurtosis404.89361
Mean23725.623
Median Absolute Deviation (MAD)9422.5
Skewness17.527561
Sum3.2209906 × 108
Variance1.0273484 × 109
MonotonicityNot monotonic
2023-10-14T12:18:11.562078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34543 68
 
0.5%
32828 64
 
0.5%
63623 25
 
0.2%
64346 24
 
0.2%
22608 24
 
0.2%
32904 24
 
0.2%
22304 22
 
0.2%
21952 21
 
0.2%
16452 21
 
0.2%
20414 20
 
0.1%
Other values (6527) 13263
97.7%
ValueCountFrequency (%)
1838 1
< 0.1%
2188 1
< 0.1%
2254 1
< 0.1%
2257 1
< 0.1%
2274 2
< 0.1%
2430 1
< 0.1%
2442 1
< 0.1%
2463 1
< 0.1%
2529 1
< 0.1%
2546 2
< 0.1%
ValueCountFrequency (%)
781647 16
0.1%
748736 2
 
< 0.1%
230069 1
 
< 0.1%
147197 1
 
< 0.1%
140949 1
 
< 0.1%
127860 2
 
< 0.1%
127042 1
 
< 0.1%
126696 1
 
< 0.1%
126559 1
 
< 0.1%
126547 1
 
< 0.1%

journey_duration
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.429213
Minimum1
Maximum442
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size212.1 KiB
2023-10-14T12:18:11.911790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q118
median25
Q334
95-th percentile49
Maximum442
Range441
Interquartile range (IQR)16

Descriptive statistics

Standard deviation19.226233
Coefficient of variation (CV)0.70094
Kurtosis279.68668
Mean27.429213
Median Absolute Deviation (MAD)8
Skewness13.373448
Sum372379
Variance369.64803
MonotonicityNot monotonic
2023-10-14T12:18:12.237781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 476
 
3.5%
25 469
 
3.5%
16 468
 
3.4%
26 468
 
3.4%
18 461
 
3.4%
22 456
 
3.4%
27 454
 
3.3%
20 448
 
3.3%
23 447
 
3.3%
29 443
 
3.3%
Other values (83) 8986
66.2%
ValueCountFrequency (%)
1 2
 
< 0.1%
4 2
 
< 0.1%
5 8
 
0.1%
6 17
 
0.1%
7 43
 
0.3%
8 113
 
0.8%
9 199
1.5%
10 205
1.5%
11 233
1.7%
12 299
2.2%
ValueCountFrequency (%)
442 16
0.1%
424 2
 
< 0.1%
177 1
 
< 0.1%
143 1
 
< 0.1%
128 1
 
< 0.1%
113 1
 
< 0.1%
104 1
 
< 0.1%
96 1
 
< 0.1%
93 2
 
< 0.1%
92 1
 
< 0.1%

has_incentive
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size901.5 KiB
NON
13544 
OUI
 
32

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters40728
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNON
2nd rowNON
3rd rowNON
4th rowNON
5th rowNON

Common Values

ValueCountFrequency (%)
NON 13544
99.8%
OUI 32
 
0.2%

Length

2023-10-14T12:18:12.561630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-14T12:18:12.865996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
non 13544
99.8%
oui 32
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N 27088
66.5%
O 13576
33.3%
U 32
 
0.1%
I 32
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 40728
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 27088
66.5%
O 13576
33.3%
U 32
 
0.1%
I 32
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 40728
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 27088
66.5%
O 13576
33.3%
U 32
 
0.1%
I 32
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40728
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 27088
66.5%
O 13576
33.3%
U 32
 
0.1%
I 32
 
0.1%

Interactions

2023-10-14T12:17:50.271258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:14.618881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:17.677306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:20.459025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:22.841902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:29.553350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:32.527902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:35.125052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:38.384572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:44.422841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:46.935533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:50.530025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:14.898523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:17.953590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:20.619921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:23.476945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:29.806138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:32.693795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:35.290221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:38.861655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:44.585469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:47.213945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:50.800567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:15.082269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:18.124490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:20.791518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:24.018835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:30.075968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:32.866090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:35.458781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:39.333617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:44.742389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:47.500282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:51.069934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:15.245227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:18.314733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:20.957158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:24.562796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:30.352911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:33.046666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:35.630290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:39.760485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:44.902464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:47.683640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:51.630936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:15.851703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:18.795453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:21.429466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:25.467335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:30.800437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:33.704427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:36.217221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:40.656525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:45.340977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:48.370496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:51.831870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:15.994544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:18.933453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:21.562267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:25.849227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:30.986697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:33.831759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:36.428294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:41.030214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:45.454005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:48.492838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:52.019650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:16.269485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:19.099661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:21.737253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:26.395396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:31.277309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:33.997150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:36.704261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:41.564990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:45.629075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:48.721681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:52.288133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:16.466471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:19.276020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:21.913288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:26.951553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:31.558920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:34.196218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:36.977887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:42.193038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:45.890062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:48.921198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:53.052158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:17.012369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:19.956781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:22.373171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:27.906362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:32.013085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:34.656869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:37.712034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:42.901678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:46.323789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:49.488421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:53.252877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:17.135215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:20.089788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:22.501444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:28.288722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:32.114608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:34.786448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:37.927972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:43.270823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:46.421341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:49.721855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:53.530205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:17.421041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:20.291793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:22.678216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:28.834761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:32.388139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:34.970299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:38.119813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:43.814513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:46.695358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-14T12:17:50.007302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-14T12:18:13.074751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
journey_idjourney_start_lonjourney_start_latjourney_start_inseejourney_start_departmentjourney_end_lonjourney_end_latjourney_end_inseejourney_end_departmentjourney_distancejourney_durationjourney_start_towngroupjourney_end_towngroupoperator_classhas_incentive
journey_id1.0000.0020.026-0.0160.014-0.0020.028-0.0030.027-0.0210.0060.0380.0200.0530.045
journey_start_lon0.0021.000-0.163-0.056-0.1820.086-0.024-0.0190.001-0.0210.0200.8530.0420.6680.753
journey_start_lat0.026-0.1631.0000.2280.542-0.0060.107-0.0360.004-0.097-0.0920.8850.1800.6670.707
journey_start_insee-0.016-0.0560.2281.0000.611-0.014-0.050-0.241-0.184-0.142-0.0870.9920.0000.6670.855
journey_start_department0.014-0.1820.5420.6111.0000.012-0.002-0.176-0.134-0.353-0.2640.9250.0630.6680.866
journey_end_lon-0.0020.086-0.006-0.0140.0121.000-0.191-0.083-0.2340.0030.0210.0740.8860.1930.334
journey_end_lat0.028-0.0240.107-0.050-0.002-0.1911.0000.2020.531-0.081-0.0550.0130.9590.2360.259
journey_end_insee-0.003-0.019-0.036-0.241-0.176-0.0830.2021.0000.598-0.138-0.1110.0000.9920.2270.482
journey_end_department0.0270.0010.004-0.184-0.134-0.2340.5310.5981.000-0.362-0.2850.0630.9990.2380.499
journey_distance-0.021-0.021-0.097-0.142-0.3530.003-0.081-0.138-0.3621.0000.8590.6570.7370.7070.765
journey_duration0.0060.020-0.092-0.087-0.2640.021-0.055-0.111-0.2850.8591.0000.6610.5140.7070.766
journey_start_towngroup0.0380.8530.8850.9920.9250.0740.0130.0000.0630.6570.6611.0000.0330.6700.865
journey_end_towngroup0.0200.0420.1800.0000.0630.8860.9590.9920.9990.7370.5140.0331.0000.2400.498
operator_class0.0530.6680.6670.6670.6680.1930.2360.2270.2380.7070.7070.6700.2401.0000.750
has_incentive0.0450.7530.7070.8550.8660.3340.2590.4820.4990.7650.7660.8650.4980.7501.000

Missing values

2023-10-14T12:17:53.989732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-14T12:17:54.827885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

journey_idtrip_idjourney_start_datetimejourney_start_datejourney_start_timejourney_start_lonjourney_start_latjourney_start_inseejourney_start_departmentjourney_start_townjourney_start_towngroupjourney_start_countryjourney_end_datetimejourney_end_datejourney_end_timejourney_end_lonjourney_end_latjourney_end_inseejourney_end_departmentjourney_end_townjourney_end_towngroupjourney_end_countrypassenger_seatsoperator_classjourney_distancejourney_durationhas_incentive
5717277395d2a0a6fe-b8fa-4a1b-aae6-a21ca99c10d32023-08-01T00:40:00+02:002023-08-0100:40:001.05949.4507654076RouenMétropole Rouen NormandieFrance2023-08-01T00:50:00+02:002023-08-0100:50:001.06649.4317649876Le Petit-QuevillyMétropole Rouen NormandieFrance1C31777NON
59172774188ed2d495-7515-4546-8cb0-19a4641221422023-08-01T00:40:00+02:002023-08-0100:40:001.06449.4467654076RouenMétropole Rouen NormandieFrance2023-08-01T01:00:00+02:002023-08-0101:00:001.17149.4017647576Franqueville-Saint-PierreMétropole Rouen NormandieFrance1C1350719NON
9817277506d2ddd2f4-bdd5-4e81-94bc-a2974a4f5dcb2023-08-01T01:50:00+02:002023-08-0101:50:001.04249.6597672176Varneville-BrettevilleCC Terroir de CauxFrance2023-08-01T02:10:00+02:002023-08-0102:10:001.06249.4517654076RouenMétropole Rouen NormandieFrance1C3009319NON
9917277507d2ddd2f4-bdd5-4e81-94bc-a2974a4f5dcb2023-08-01T01:50:00+02:002023-08-0101:50:001.04249.6597672176Varneville-BrettevilleCC Terroir de CauxFrance2023-08-01T02:10:00+02:002023-08-0102:10:001.06249.4517654076RouenMétropole Rouen NormandieFrance1C3009319NON
1131727754674fe92cd-c8c8-4e75-89ab-89408f3e2ffd2023-08-01T02:20:00+02:002023-08-0102:20:001.13949.4127642976Le Mesnil-EsnardMétropole Rouen NormandieFrance2023-08-01T02:30:00+02:002023-08-0102:30:001.08849.4397654076RouenMétropole Rouen NormandieFrance1C566715NON
154172778750a5d78f0-ef23-4824-9c6f-e0e0d91d8af02023-08-01T03:30:00+02:002023-08-0103:30:001.09049.4487654076RouenMétropole Rouen NormandieFrance2023-08-01T03:50:00+02:002023-08-0103:50:001.14149.4987637776IsneauvilleMétropole Rouen NormandieFrance1C981219NON
38017278265b39ed579-c82b-4fdd-bc7e-32c8c245bd0d2023-08-01T04:30:00+02:002023-08-0104:30:001.06949.4397654076RouenMétropole Rouen NormandieFrance2023-08-01T05:00:00+02:002023-08-0105:00:000.79549.4817670976Le TraitMétropole Rouen NormandieFrance1C2801827NON
66117278370aa851fa6-5b68-4ca8-a9ad-34f2b43d1fae2023-08-01T05:10:00+02:002023-08-0105:10:001.03649.4237632276Le Grand-QuevillyMétropole Rouen NormandieFrance2023-08-01T05:30:00+02:002023-08-0105:30:001.13249.4347654076RouenMétropole Rouen NormandieFrance1C952520NON
707172785651ae3503e-c236-41cc-8944-908423e3bf602023-08-01T05:20:00+02:002023-08-0105:20:000.82049.5637616076Carville-la-FolletièreCC Yvetot NormandieFrance2023-08-01T05:50:00+02:002023-08-0105:50:001.08049.4347654076RouenMétropole Rouen NormandieFrance1C2805431NON
7501727863538a63869-d37c-4a94-b684-8916858446b82023-08-01T05:20:00+02:002023-08-0105:20:000.85049.3612709127BosgouetCC Roumois SeineFrance2023-08-01T06:00:00+02:002023-08-0106:00:001.10749.4417654076RouenMétropole Rouen NormandieFrance1C3027839NON
journey_idtrip_idjourney_start_datetimejourney_start_datejourney_start_timejourney_start_lonjourney_start_latjourney_start_inseejourney_start_departmentjourney_start_townjourney_start_towngroupjourney_start_countryjourney_end_datetimejourney_end_datejourney_end_timejourney_end_lonjourney_end_latjourney_end_inseejourney_end_departmentjourney_end_townjourney_end_towngroupjourney_end_countrypassenger_seatsoperator_classjourney_distancejourney_durationhas_incentive
50618917807250f311ead1-e6fe-41d1-b914-ce7caa52c5bf2023-08-31T22:20:00+02:002023-08-3122:20:001.05549.4537654076RouenMétropole Rouen NormandieFrance2023-08-31T22:40:00+02:002023-08-3122:40:001.03849.5267640276MalaunayMétropole Rouen NormandieFrance1C1869820NON
50629517830294d433e91d-6b50-4bb4-b565-128d8bc9fffd2023-08-31T22:40:00+02:002023-08-3122:40:001.08649.4407654076RouenMétropole Rouen NormandieFrance2023-08-31T23:10:00+02:002023-08-3123:10:001.17449.2212737527LouviersCA Seine-EureFrance1C3491427NON
50636217807422841f2d99-7a5c-4dc5-8e67-8b2c57cbaac42023-08-31T23:00:00+02:002023-08-3123:00:001.09049.4337654076RouenMétropole Rouen NormandieFrance2023-08-31T23:10:00+02:002023-08-3123:10:001.03449.4177632276Le Grand-QuevillyMétropole Rouen NormandieFrance1C583211NON
506403178076251254381e-29c1-41a7-9b25-8e5e31b60ce42023-08-31T23:10:00+02:002023-08-3123:10:001.08149.4337654076RouenMétropole Rouen NormandieFrance2023-08-31T23:30:00+02:002023-08-3123:30:001.10349.3897657576Saint-Étienne-du-RouvrayMétropole Rouen NormandieFrance1C593120NON
506404178076271254381e-29c1-41a7-9b25-8e5e31b60ce42023-08-31T23:10:00+02:002023-08-3123:10:001.08149.4337654076RouenMétropole Rouen NormandieFrance2023-08-31T23:30:00+02:002023-08-3123:30:001.10349.3897657576Saint-Étienne-du-RouvrayMétropole Rouen NormandieFrance1C593120NON
506405178076291254381e-29c1-41a7-9b25-8e5e31b60ce42023-08-31T23:10:00+02:002023-08-3123:10:001.08149.4337654076RouenMétropole Rouen NormandieFrance2023-08-31T23:30:00+02:002023-08-3123:30:001.10349.3897657576Saint-Étienne-du-RouvrayMétropole Rouen NormandieFrance1C593120NON
50640617807649f736ad6b-9b8d-4f97-a7b2-e695ebd07f342023-08-31T23:10:00+02:002023-08-3123:10:001.10749.4307654076RouenMétropole Rouen NormandieFrance2023-08-31T23:30:00+02:002023-08-3123:30:001.08649.3937657576Saint-Étienne-du-RouvrayMétropole Rouen NormandieFrance1C831218NON
50644417807741126d8fa1-3d70-494b-9946-520bde64f6522023-08-31T23:20:00+02:002023-08-3123:20:001.08449.4227654076RouenMétropole Rouen NormandieFrance2023-08-31T23:50:00+02:002023-08-3123:50:001.36149.4817657376Saint-Denis-le-ThiboultCC Inter-Caux-VexinFrance1C2429831NON
506520178077430626382a-ded0-4297-92a4-b0999753832a2023-08-31T23:50:00+02:002023-08-3123:50:001.09249.4397654076RouenMétropole Rouen NormandieFrance2023-09-01T00:00:00+02:002023-09-0100:00:001.20249.4407659176Saint-Jacques-sur-DarnétalMétropole Rouen NormandieFrance1C966517NON
5065211780774574f6b016-c4bb-4be8-89fe-9246f423464f2023-08-31T23:50:00+02:002023-08-3123:50:001.09249.4397654076RouenMétropole Rouen NormandieFrance2023-09-01T00:00:00+02:002023-09-0100:00:001.20249.4407659176Saint-Jacques-sur-DarnétalMétropole Rouen NormandieFrance1C966516NON